import collections
import copy
import functools
import itertools
import unittest
from typing import Any, List, Optional, Type, Union

import torch
import torch.distributed as dist
import torch.nn as nn
from torch.distributed.fsdp import fully_shard
from torch.nn.parallel.scatter_gather import _is_namedtuple
from torch.testing._internal.common_fsdp import (
    check_sharded_parity,
    DoubleLinear,
    FSDPTestMultiThread,
    MLP,
)
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
    ModelArgs,
    Transformer,
)

import torch_npu
from torch_npu.testing.common_utils import SupportedDevices
from torch_npu.testing._internal.common_fsdp import FSDPNPUTest

torch.use_deterministic_algorithms(True)


class TestFullyShardAutograd(FSDPNPUTest):
    @property
    def world_size(self) -> int:
        return min(4, torch.npu.device_count())

    def _reduce_1d_partial_grads(
        self, module: nn.Module, group: Optional[dist.ProcessGroup] = None
    ) -> None:
        group = group or dist.distributed_c10d._get_default_group()
        for param in module.parameters():
            if param.grad is not None:
                param.grad.div_(group.size())

    def test_unused_forward_output(self):
        """
        Tests that gradients propagate when running a backward where some
        forward output is not used to compute the loss.
        """
        self.run_subtests(
            {"reshard_after_forward": [True, False, 2]},
            self._test_unused_forward_output,
        )

    def _test_unused_forward_output(self, reshard_after_forward: Union[bool, int]):
        torch.manual_seed(42)
        local_batch_size = 2
        global_batch_size, dim = (self.world_size * local_batch_size, 24)
        model = DoubleLinear(dim=dim, use_second_linear=True)
        ref_model = copy.deepcopy(model).npu()
        fully_shard(model.lin1, reshard_after_forward=reshard_after_forward)
        fully_shard(model, reshard_after_forward=reshard_after_forward)
        ref_optim = torch.optim.Adam(ref_model.parameters(), lr=1e-2)
        optim = torch.optim.Adam(model.parameters(), lr=1e-2)

        torch.manual_seed(1)  # same on all ranks
        for iter_idx in range(10):
            # Use all forward outputs in the loss/backward for the first half
            # of the iterations and only the 1st forward output for the rest
            global_inp = torch.rand((global_batch_size, dim), device="npu")
            local_inp = global_inp[self.rank * local_batch_size:(self.rank + 1) * local_batch_size].detach()
            out1, out2 = model(local_inp)
            loss = (out1 * out2).sum() if iter_idx < 3 else out1.sum()
            loss.backward()
            optim.step()
            ref_out1, ref_out2 = ref_model(global_inp)
            ref_loss = (ref_out1 * ref_out2).sum() if iter_idx < 3 else ref_out1.sum()
            ref_loss.backward()
            self._reduce_1d_partial_grads(ref_model)
            ref_optim.step()
            dist.all_reduce(loss)  # partial -> replicated
            self.assertEqual(loss, ref_loss)
            optim.zero_grad(set_to_none=(iter_idx % 2))
            ref_optim.zero_grad(set_to_none=(iter_idx % 2))
            check_sharded_parity(self, ref_model, model)

    def test_unused_forward_module(self):
        """
        Tests that gradients propagate when running a backward where some
        forward module is not used to compute the loss.
        """
        self.run_subtests(
            {"reshard_after_forward": [True, False, 2]},
            self._test_unused_forward_module,
        )

    def _test_unused_forward_module(self, reshard_after_forward: Union[bool, int]):
        torch.manual_seed(42)
        local_batch_size, dim = (2, 24)
        global_batch_size = self.world_size * local_batch_size
        model = DoubleLinear(dim=dim, use_second_linear=False)
        ref_model = copy.deepcopy(model).npu()
        fully_shard(model.lin1, reshard_after_forward=reshard_after_forward)
        fully_shard(model.lin2, reshard_after_forward=reshard_after_forward)
        fully_shard(model, reshard_after_forward=reshard_after_forward)
        ref_optim = torch.optim.Adam(ref_model.parameters(), lr=1e-2)
        optim = torch.optim.Adam(model.parameters(), lr=1e-2)

        torch.manual_seed(1)  # same on all ranks
        for iter_idx in range(10):
            global_inp = torch.rand((global_batch_size, dim), device="npu")
            local_inp = global_inp[self.rank * local_batch_size:(self.rank + 1) * local_batch_size].detach()
            losses: List[torch.Tensor] = []
            for _model, inp in ((ref_model, global_inp), (model, local_inp)):
                losses.append(_model(inp).sum())
                losses[-1].backward()
            self._reduce_1d_partial_grads(ref_model)
            dist.all_reduce(losses[1])  # partial -> replicated
            self.assertEqual(losses[0], losses[1])
            check_sharded_parity(self, ref_model, model)
            for _optim in (optim, ref_optim):
                _optim.step()
                _optim.zero_grad(set_to_none=(iter_idx % 2))

    def test_nontensor_activations(self):
        """
        Tests that gradients propagate when running forward with nontensor
        data structures wrapping the activations. This is mainly to test the
        hook registration.
        """
        self.run_subtests(
            {"container_type": [list, collections.namedtuple, tuple, dict]},
            self._test_nontensor_activations,
        )

    def _test_nontensor_activations(self, container_type: Type):
        class Module(nn.Module):
            def __init__(self, dim: int):
                super().__init__()
                self.lin1 = nn.Linear(dim, dim)
                self.lin2 = nn.Linear(dim, dim)
                self.relu = nn.ReLU()

            def forward(self, inp: Any):
                # Assume that the "0th" element of `inp` is a tensor, run some
                # forward computation on it, and pack it back into the same
                # data structure type as `inp`
                if isinstance(inp, list):
                    return [self._forward(inp[0])]
                elif _is_namedtuple(inp):
                    return type(inp)(*([self._forward(inp[0])] + list(inp[1:])))
                elif isinstance(inp, tuple):
                    return (self._forward(inp[0]),)
                elif isinstance(inp, dict):
                    return {"x": self._forward(inp["x"])}
                else:
                    raise NotImplementedError(
                        f"Unsupported input type {type(inp)}: {inp}"
                    )

            def _forward(self, x: torch.Tensor) -> torch.Tensor:
                return self.relu(self.lin2(self.relu(self.lin1(x))))

        class ToContainerType(nn.Module):
            def __init__(self, container_type: Type):
                super().__init__()
                self.container_type = container_type

            def forward(self, x: torch.Tensor):
                if self.container_type is list:
                    return [x]
                elif self.container_type is collections.namedtuple:
                    nt = collections.namedtuple("NT", "x y")
                    return nt(x, torch.ones_like(x))
                elif self.container_type is tuple:
                    return (x,)
                elif self.container_type is dict:
                    return {"x": x}
                else:
                    raise NotImplementedError(
                        f"Unsupported container type: {self.container_type}"
                    )

        class FromContainerType(nn.Module):
            def __init__(self, container_type: Type):
                super().__init__()
                self.container_type = container_type

            def forward(self, x: torch.Tensor):
                if self.container_type in (list, collections.namedtuple, tuple):
                    return x[0]
                elif self.container_type is dict:
                    return x["x"]
                else:
                    raise NotImplementedError(
                        f"Unsupported container type: {self.container_type}"
                    )

        torch.manual_seed(42)
        local_batch_size, dim = (2, 24)
        global_batch_size = self.world_size * local_batch_size
        model = nn.Sequential(
            ToContainerType(container_type),
            Module(dim),
            Module(dim),
            Module(dim),
            FromContainerType(container_type),
        )
        ref_model = copy.deepcopy(model).npu()
        for module in model:
            fully_shard(module)
        fully_shard(model)
        ref_optim = torch.optim.Adam(ref_model.parameters(), lr=1e-2)
        optim = torch.optim.Adam(model.parameters(), lr=1e-2)

        torch.manual_seed(1)  # same on all ranks
        for iter_idx in range(10):
            global_inp = torch.rand((global_batch_size, dim), device="npu")
            local_inp = global_inp[self.rank * local_batch_size:(self.rank + 1) * local_batch_size].detach()
            losses: List[torch.Tensor] = []
            for _model, inp in ((ref_model, global_inp), (model, local_inp)):
                losses.append(_model(inp).sum())
                losses[-1].backward()
            self._reduce_1d_partial_grads(ref_model)
            dist.all_reduce(losses[1])  # partial -> replicated
            self.assertEqual(losses[0], losses[1])
            check_sharded_parity(self, ref_model, model)
            for _optim in (optim, ref_optim):
                _optim.step()
                _optim.zero_grad(set_to_none=(iter_idx % 2))


class TestFullyShardPostAccGradHookMultiThread(FSDPTestMultiThread):
    @property
    def world_size(self) -> int:
        return 2

    def perThreadSetUp(self):
        super().perThreadSetUp()
        torch.npu.set_device(0)

    @SupportedDevices(['Ascend910B'])
    def test_post_acc_grad_hook_runs(self):
        param_name_to_hook_count = collections.defaultdict(int)

        def hook(param_name: str, param: torch.Tensor) -> None:
            nonlocal param_name_to_hook_count
            param_name_to_hook_count[param_name] += 1

        model = MLP(8)
        for module in (model.in_proj, model.out_proj, model):
            fully_shard(module)
        for param_name, param in model.named_parameters():
            param_hook = functools.partial(hook, param_name)
            param.register_post_accumulate_grad_hook(param_hook)

        inp = torch.randn((2, 8), device="npu")
        model(inp).sum().backward()
        param_names = {param_name for param_name, _ in model.named_parameters()}
        self.assertEqual(param_names, set(param_name_to_hook_count.keys()))
        for _, count in param_name_to_hook_count.items():
            self.assertEqual(count, 1)


class TestFullyShardPostAccGradHookMultiProcess(FSDPNPUTest):
    @property
    def world_size(self) -> int:
        return min(torch.npu.device_count(), 2)

    @SupportedDevices(['Ascend910B'])
    def test_post_acc_grad_hook_optim_parity(self):
        """
        Tests parity of running the optimizer via the post-accumulate-grad
        hook vs. normally.
        """
        torch.manual_seed(42)
        model_args = ModelArgs(dropout_p=0.0)
        model = Transformer(model_args)

        ref_model = copy.deepcopy(model).npu()
        for module in itertools.chain(ref_model.layers, [ref_model]):
            fully_shard(module)
        optim_kwargs = {"lr": 1e-2, "foreach": False}
        ref_optim = torch.optim.AdamW(ref_model.parameters(), **optim_kwargs)
        lr_scheduler_kwargs = {"step_size": 5}
        ref_lr_scheduler = torch.optim.lr_scheduler.StepLR(
            ref_optim, **lr_scheduler_kwargs
        )

        for module in itertools.chain(model.layers, [model]):
            fully_shard(module)
        param_to_optim = {}
        param_to_lr_scheduler = {}
        for param in model.parameters():
            param_to_optim[param] = torch.optim.AdamW([param], **optim_kwargs)
            param_to_lr_scheduler[param] = torch.optim.lr_scheduler.StepLR(
                param_to_optim[param], **lr_scheduler_kwargs
            )

        def optim_hook(param: nn.Parameter) -> None:
            param_to_optim[param].step()
            param_to_optim[param].zero_grad()
            param_to_lr_scheduler[param].step()

        for param in model.parameters():
            param.register_post_accumulate_grad_hook(optim_hook)

        torch.manual_seed(42 + self.rank)
        inp = torch.randint(0, model_args.vocab_size, (2, 16), device="npu")
        for _ in range(10):
            ref_loss = ref_model(inp).sum()
            ref_loss.backward()
            ref_optim.step()
            ref_optim.zero_grad()
            ref_lr_scheduler.step()
            loss = model(inp).sum()
            loss.backward()
            self.assertTrue(torch.equal(ref_loss, loss))
            for ref_param, param in zip(ref_model.parameters(), model.parameters()):
                self.assertTrue(torch.equal(ref_param, param))


if __name__ == "__main__":
    run_tests()
